Overweight, obesity and the risk of LADA: results from a Swedish case–control study and the Norwegian HUNT Study

Overweight, obesity and the risk of LADA: results from a Swedish case-control study and the Norwegian HUNT Study
Rebecka Hjort 0 1 2 3 4 5 6 7 8
Emma Ahlqvist 0 1 2 3 4 5 6 7 8
Per-Ola Carlsson 0 1 2 3 4 5 6 7 8
Valdemar Grill 0 1 2 3 4 5 6 7 8
Leif Groop 0 1 2 3 4 5 6 7 8
Mats Martinell 0 1 2 3 4 5 6 7 8
Bahareh Rasouli 0 1 2 3 4 5 6 7 8
Anders Rosengren 0 1 2 3 4 5 6 7 8
Tiinamaija Tuomi 0 1 2 3 4 5 6 7 8
Bjørn Olav Åsvold 0 1 2 3 4 5 6 7 8
Sofia Carlsson 0 1 2 3 4 5 6 7 8
Rebecka Hjort 0 1 2 3 4 5 6 7 8
0 Department of Medical Sciences, Uppsala University , Uppsala , Sweden
1 Department of Clinical Sciences in Malmö, Clinical Research Centre, Lund University , Malmö , Sweden
2 Unit of Epidemiology, Institute of Environmental Medicine, Karolinska Institutet , Box 210, 171 77 Stockholm , Sweden
3 K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology , Trondheim , Norway
4 Folkhälsan Research Center , Helsinki , Finland
5 Division of Endocrinology, Abdominal Center, Helsinki University Hospital, Research Program for Diabetes and Obesity, University of Helsinki , Helsinki , Finland
6 Department of Public Health and Caring Sciences, Uppsala University , Uppsala , Sweden
7 Department of Endocrinology, St Olavs Hospital, Trondheim University Hospital , Trondheim , Norway
8 Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology , Trondheim , Norway
Aims/hypothesis Excessive weight is a risk factor for type 2 diabetes, but its role in the promotion of autoimmune diabetes is not clear. We investigated the risk of latent autoimmune diabetes in adults (LADA) in relation to overweight/obesity in two large population-based studies. Methods Analyses were based on incident cases of LADA (n = 425) and type 2 diabetes (n = 1420), and 1704 randomly selected control participants from a Swedish case-control study and prospective data from the Norwegian HUNT Study including 147 people with LADA and 1,012,957 person-years of follow-up (1984-2008). We present adjusted ORs and HRs with 95% CI. Results In the Swedish data, obesity was associated with an increased risk of LADA (OR 2.93, 95% CI 2.17, 3.97), which was even stronger for type 2 diabetes (OR 18.88, 95% CI 14.29, 24.94). The association was stronger in LADA with low GAD antibody (GADA; <median) (OR 4.25; 95% CI 2.76, 6.52) but present also in LADA with high GADA (OR 2.14; 95% CI 1.42, 3.24). In the Swedish data, obese vs normal weight LADA patients had lower GADA levels, better beta cell function, and were more likely to have low-risk HLA-genotypes. The combination of overweight and family history of diabetes (FHD) conferred an OR of 4.57 (95% CI 3.27, 6.39) for LADA and 24.51 (95% CI 17.82, 33.71) for type 2 diabetes. Prospective data from HUNT indicated even stronger associations; HR for LADA was 6.07 (95% CI 3.76, 9.78) for obesity and 7.45 (95% CI 4.02, 13.82) for overweight and FHD. Conclusions/interpretation Overweight/obesity is associated with increased risk of LADA, particularly when in combination with FHD. These findings support the hypothesis that, even in the presence of autoimmunity, factors linked to insulin resistance, such as excessive weight, could promote onset of diabetes.
ANDIS; ANDiU; Body mass index; Case-control study; ESTRID; HUNT Study; LADA; Latent autoimmune diabetes in adults; Prospective study; Type 2 diabetes
-
Finnish Institute of Molecular Medicine, University of Helsinki,
Helsinki, Finland
Abbreviations
ANDIS
ANDiU
ESTRID
FHD
GADA
HUNT Study
LADA
LADAhigh
LADAlow
PAR
WHtR
All New Diabetics In Scania
All New Diabetics in Uppsala County
Epidemiological Study of Risk Factors
for LADA and Type 2 diabetes
Family history of diabetes
GAD antibody
Nord-Trøndelag Health Study
Latent autoimmune diabetes in adults
LADA group with high median GADA
LADA group with low median GADA
Population-attributable risk
Waist-to-height ratio
Introduction
Overweight and obesity are major risk factors for type 2
diabetes [1], and the association between excessive weight and
insulin resistance is well known. Several mediating pathways
have been proposed, including ectopic lipid accumulation and
lipotoxicity, and the release of proinflammatory cytokines
from visceral fat tissue [2].
Type 1 diabetes has been viewed as a non-obese form of
diabetes, but this was challenged by the accelerator hypothesis
[3], which proposes that obesity accelerates disease onset and,
further, that insulin resistance is a common underlying feature
of all types of diabetes [3]. Insulin resistance has also been
postulated to be an independent risk factor for type 1 diabetes
[4, 5]. Adiposity could potentially affect risk via beta cell
autoimmunity; adipokines, which are secreted from
excessive fat tissue, have been shown to be involved in various
immune-mediating processes [6]. Subsequent prospective
studies have reported a twofold increased risk of type 1
diabetes in obese children [7] and obese adults [8], while
others find no association [9]. An association is further
supported by the coincident increases in childhood
obesity and type 1 diabetes incidence [10, 11].
Latent autoimmune diabetes in adults (LADA) is an
autoimmune form of diabetes with features of type 2 diabetes,
including adult onset and insulin resistance [12].
Autoimmunity is typically less pronounced than in type 1
diabetes, which implies that insulin resistance, increasing the
beta cell demand, may play a key role in the promotion of
LADA. In line with this, data from cross-sectional studies
[
13–19
] suggest that individuals with LADA tend to have
higher BMI than those with type 1 diabetes, but lower than
those with type 2 diabetes. Interestingly, the clinical
phenotype of LADA is known to vary by degree of autoimmunity,
with less autoimmune individuals being more type 2-like.
Hence, the role of overweight in the development of LADA
may depend on the severity of the underlying autoimmune
process. Only one prospective study based on 11 years
follow-up of the Norwegian Nord-Trøndelag Health Study
(HUNT Study) estimated the risk of LADA in relation to
overweight/obesity [20]. This study was based on only 81
individuals; hence, the influence of excessive weight on more
or less autoimmune forms of LADA could not be explored,
and confounding control was limited. Other aspects that
remain to be addressed include interaction between overweight
and family history of diabetes (FHD), which is a strong
indicator of type 2 diabetes risk [21], whether the shape of
association is linear or not, and the preventive potential of
overweight in the aetiology of LADA.
Our aim was to describe the association between
overweight and obesity and LADA compared with type 2 diabetes,
taking into account degree of autoimmunity and potential
interaction with FHD. We used updated prospective data from
the HUNT Study, including 22 years of follow-up, and newly
collected data from a Swedish case–control study with
incident cases.
Methods
The ESTRID study
Study population and design The Epidemiological Study of
Risk Factors for LADA and Type 2 Diabetes (ESTRID) is an
ongoing population-based case–control study [22]. In short,
ESTRID is a substudy of All New Diabetics In Scania
(ANDIS; http://andis.ludc.med.lu.se), an extensive diabetes
study aimed at characterising clinical and genetic factors in
incident cases in Scania. Since 2010, we have recruited all
incident cases of LADA and a random sample of those with
type 2 diabetes (four for every one person with LADA) from
ANDIS. Since 2012, we have also included individuals from
ANDiU (All New Diabetics in Uppsala County; www.andiu.
se/), a sister study to ANDIS. Control participants (six for
every one person with LADA [≥35 years of age]) without
diabetes are randomly selected from the national population
register and matched to the case for county and time of
recruitment (incident density sampling) [23].
The analytical sample for the present study consisted of all
individuals recruited until July 2016 and with complete
information on BMI, age, sex, FHD, physical activity and smoking
(98.2% of the study sample): 425 individuals with LADA,
1420 individuals with type 2 diabetes and 1704 control
participants (95% of participants came from Scania and 5% came
from Uppsala). The response rate was 80% for the individuals
with LADA and type 2 diabetes and 64% for control
participants. ESTRID was approved by the ethical review board in
Stockholm and all participants gave written informed consent.
Case definition and biochemical analyses At diagnosis, blood
samples were collected from all individuals and analysed for
GAD antibody (GADA) in serum using ELISA (RSR,
Cardiff, UK). At the cut-off level for positivity (10 U/ml),
sensitivity was 84% and specificity 98% [24]. Fasting
Cpeptide was assessed in plasma using IMMULITE 2000
(Siemens Healthcare Diagnostics, Llanberis, UK) or Cobas e
601 (Roche Diagnostics, Mannheim, Germany).
Individuals with LADA had diagnosis ≥35 years of
age, were GADA positive (≥10 U/ml) and had
Cpeptide levels above the lower limit for the normal range
≥0.2 nmol/l (IMMULITE) or ≥0.3 nmol/l (Cobas e 601).
Type 2 diabetes was defined as onset ≥35 years of age,
GADA negativity (<10 U/ml) and C-peptide levels
>0.6 nmol/l (IMMULITE) or ≥0.72 nmol/l (Cobas e
601). DNA was analysed using iPLEX Gold technology
(Sequenom Laboratories, San Diego, CA, USA). Three
SNPs in the MHC region (rs3104413, rs2854275,
rs9273363) were combined to identify high- and
lowrisk HLA-DR and HLA-DQ genotypes associated with
autoimmunity [25], according to previously used
methods [26]. Missing genotypes were completed using
imputed data from an ANDIS subset genotyped on
Infinium CoreExome v1.1 (Illumina, San Diego, CA,
USA), imputed based on the Haplotype Reference
Consortium (http://www.haplotype-reference-consortium.
org/; version r1.1 2016) reference panel. HOMA was
used to estimate insulin resistance, insulin sensitivity
and beta cell function based on the relationship
between fasting values of C-peptide and plasma glucose
[
27
]. No genetic or clinical information was available for
the control participants.
BMI and covariates Case and control participants answered an
extensive questionnaire at inclusion. For those with LADA or
type 2 diabetes, this was done as close to diagnosis as possible
(median 5 months), with careful instructions to report lifestyle
as it was prior to diagnosis. Current BMI was based on
selfreported weight and height, which shows high correlation (r =
0.92) with BMI based on measurements taken at diagnosis
(those with LADA or type 2 diabetes). BMI was categorised
as: normal weight <25 kg/m2, overweight 25–29.9 kg/m2 and
obese ≥30 kg/m2 (WHO). BMI at age 20 years was calculated
based on self-reported information on weight at age 20 years
(80.4% of the study sample could recall this information) and
current height. FHD was obtained from questions on diabetes
in first-degree relatives (mother, father, sisters, brothers and
children). Relatives with onset <40 years of age and with
insulin treatment were considered to have type 1 diabetes,
otherwise they were judged to have type 2 diabetes. Physical
activity level (sedentary, low, moderate or high activity) was
assessed from validated questions [
28
] on leisure time activity.
Individuals were categorised based on highest achieved
education (primary school, upper secondary school, university)
and through detailed questions on lifetime smoking as never,
former or current smokers. Alcohol habits were categorised
into four groups (ranging from abstainers to high consumers),
based on questions on amount and frequency of wine, beer
and liquor intake.
The HUNT Study
Study population and design In the county of
NordTrøndelag, all residents ≥20 years of age were invited to
participate in the HUNT Study on three occasions
between 1984 and 2008: HUNT1 (1984–1986), HUNT2
(1995–1997) and HUNT3 (2006–2008) [
29
]. At each
survey, data for participants were gathered from clinical
examinations, anthropometrical measurements and
comprehensive questionnaires with questions on general health,
FHD and lifestyle. Analyses were based on all individuals
who participated in at least two surveys, were free of
diabetes at baseline and with complete information on
BMI, age, sex, FHD, physical activity and smoking (n =
56, 549). The HUNT Stud y was appro ved by the
Norwegian Data Protection Authority and the Regional
Committee for Medical and Health Research Ethics and
participants gave informed consent.
Case definition Incident cases were identified by self-report
of diabetes and age at diagnosis. This self-report has high
validity when compared with medical records [
30
].
Individuals with self-reported diabetes at HUNT2 or
HUNT3 were invited for fasting blood sampling. Level of
GADA, reported as an index value in relation to standard
serum, was measured in fasting serum samples by
immunoprecipitation radioligand assay using translation-labelled
[3H]GAD65 as a labelled reagent (Novo Nordisk,
Bagsvaerd, Denmark). The sensitivity and specificity of
the assay were 0.64 and 1.00 at cut-off ≥0.08 [
31
].
Individuals were classified as having LADA if they were
aged ≥35 years at diagnosis and GADA positive (≥0.08
antibody index [WHO; ≥43 U/ml]; n = 147). This implies
that we have included individuals with adult-onset type 1
diabetes. The proportion accounted for by these
individuals is likely to be small as, when we used information
on treatment (available for 83.5% of the total study
population), 82.7% (n = 105) of those with GADA positivity did
not report insulin treatment during the year of diagnosis.
For convenience, this group will be referred to as LADA;
subanalysis based on a stricter definition of LADA (no
insulin treatment) has been conducted. Individuals with
type 2 diabetes were ≥35 years of age and GADA negative
(<43 U/ml; n = 2002). C-peptide (nmol/l) (not from time of
diagnosis) was measured in fasting serum samples and
analysed by RIA (Diagnostic Systems Laboratories,
Webster, TX, USA). HOMA indicators were calculated
based on fasting C-peptide and glucose as described above.
BMI, WHR and covariates BMI was calculated from weight
and height measured at the clinical examination. Waist and
hip circumference (only available from baseline in HUNT2)
and height were used to calculate WHR and waist-to-height
ratio (WHtR). The measures were dichotomised according to
previously used risk levels [
32
]. Those with self-reported
FHD in any of the three surveys were considered to have
FHD. Baseline information (HUNT1 or HUNT2) was used
to classify individuals according to leisure-time physical
activity (sedentary, low, moderate or high activity),
highestattained education (primary school, upper secondary school,
university), smoking status (never, former, current) and
alcohol consumption (abstainers, low, moderate or high
consumers).
Statistical analyses
Baseline characteristics were expressed as proportions, means
(SD), or medians (interquartile range [IQR]). Two-sided
p values were calculated using χ2 (proportions), Student’s t
(means) and Kruskal–Wallis (medians). ORs with 95% CIs
were calculated by conditional logistic regression for case–
control data (ESTRID) and HRs with CIs were calculated by
proportional Cox regression for prospective data (HUNT). As
control participants were sampled with an incidence density
method, the ORs can be interpreted as incidence rate ratios
[23]. In HUNT, person-years were calculated from age at
study entry until age at onset of diabetes, death or age at end
of the follow-up (either HUNT2 in 1997 or HUNT3 in 2008),
whichever came first. Time-dependent variables were used,
hence for individuals participating both in HUNT1 and
HUNT2, information on exposure and covariates was updated
at the second time of participation.
To explore the relationship between BMI and diabetes, we
used restricted cubic spline models to allow fitting of a
smooth curve without assumption about linearity [
33
],
modelled with five knots at equally spaced percentiles of
the marginal distribution of BMI. BMI was truncated below
15 kg/m2 and above 45 kg/m2 to remove the influence of
outliers. The relationship between BMI and insulin resistance
(loge HOMA-IR) and loge GADA was assessed by linear
regression. Interaction was defined as departure from
additivity of effects [
34
] and tested by calculating attributable
proportion due to interaction together with 95% CI [
35
].
Population-attributable risk (PAR) was calculated with the
formula: p(1-[1/RR]) where p is the prevalence (%) of the
risk factor of interest among cases and RR is the adjusted
OR (ESTRID) or HR (HUNT) [
36
]. All analyses were
adjusted for by age (underlying timescale in the Cox model),
sex, first-degree FHD, physical activity and smoking.
Adjustment for alcohol intake and education had minor
effects on the risk estimates (<10% change) and were not
included in the final model. Individuals with LADA were
stratified by median GADA level (196.0 U/ml [ESTRID] and
134.4 U/ml [HUNT]), referred to in the paper as LADAlow
and LADAhigh. Statistical Analysis Software (SAS) 9.4 (SAS
Institute, Cary, NC, USA) or Stata Statistical Software 14
Overweight, obesity and LADA
In ESTRID, the OR for LADA was 2.93 (95% CI 2.17, 3.97)
among obese compared with normal weight participants
(Table 2). The association seemed stronger in LADAlow (OR
4.25, 95% CI 2.76, 6.52) than in LADAhigh (OR 2.14, 95% CI
1.42, 3.24). Prospective data from HUNT indicated similar but
stronger associations; the HR associated with obesity was 6.07
(95% CI 3.76, 9.78) for LADA, 10.00 (95% CI 4.34, 23.03)
for LADAlow and 4.58 (95% CI 2.49, 8.45) for LADAhigh.
Abdominal obesity (HUNT) increased the risk of LADA
nearly twofold (HR 1.89, 95% CI 1.03, 3.46) measured with WHR
and threefold (HR 3.14, 95% CI 1.56, 6.30) measured with
WHtR (Table 2) (HUNT). Results from HUNT were similar
with a stricter definition of LADA (no insulin treatment); HR
was 6.63 (95% CI 3.67, 12.00) for obesity. LADA was also
associated with weight change over time; for every unit
increase in BMI since age 20 years, OR increased by 10% (OR
1.10, 95% CI 1.07, 1.14) (Table 2). The association between
BMI and LADA was similar in men and women (ESM
Tables 2 and 3).
LADA
pa
LADA
pa
Results
Characteristics
(StataCorp, College Station, TX, USA) (for calculating
splines) were used for the statistical analyses.
In both populations, individuals with LADA were younger at
diagnosis, had lower C-peptide concentrations and were more
often on insulin treatment than individuals with type 2
diabetes (Table 1). In ESTRID, individuals with LADA had a lower
level of insulin resistance (HOMA) and had a higher
proportion of high-risk HLA genotypes and FHD of type 1 diabetes.
Individuals with LADA were leaner than those with type 2
diabetes, whereas in HUNT, there was no corresponding
difference (Table 1). However, mean WHR was higher in
individuals with type 2 diabetes. Comparing LADAlow and
LADAhigh, the former group displayed higher concentrations
of C-peptide and better beta cell function but a higher level of
insulin resistance (ESM Table 1).
a p for difference between LADA and type 2 diabetes
b Median 5 months after diabetes diagnosis for cases in ESTRID
c Information only available from baseline at HUNT2 (1995–1997)
d Clinical information was available for 92.6% of the individuals in ESTRID (LADA n = 394, type 2 diabetes n = 1315) and 70.7% of participants in
HUNT (LADA n = 118, type 2 diabetes n = 1401)
e Genetic information was available for 90.4% of the individuals in ESTRID (LADA n = 389, type 2 diabetes n = 1278)
T1D, type 1 diabetes; T2D, type 2 diabetes
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ID I
Overweight, obesity and type 2 diabetes
The association between overweight/obesity and type 2
diabetes was stronger than for LADA, obesity was associated
with OR of 18.88 (95% CI 14.29, 24.94) in ESTRID and
HR was 9.83 (95% CI 8.49, 11.38) in HUNT (Table 2).
Abdominal obesity was associated with type 2 diabetes, HR
3.57 (95% CI 3.11, 4.09; WHR) and HR 5.08 (95% CI 4.21,
6.12; WHtR). For every BMI unit increase since age 20 years,
OR for type 2 diabetes increased by 27% (OR 1.27, 95% CI
1.23, 1.30) (ESTRID).
Restricted cubic spline analyses
Restricted cubic spline models were used to explore the
potential linear relationship between BMI and diabetes (Fig. 1).
For type 2 diabetes, a strong linear association was seen over
the whole range of BMI with a slight levelling off above BMI
27 kg/m2. For LADA, a linear pattern was less pronounced
with a tendency of a U-shaped relationship; however, above
BMI 24 kg/m2 the OR increased exponentially. A similar
shape was seen for both LADAhigh and LADAlow, but with
an apparently steeper line for the latter group.
Interaction between overweight and FHD
Individuals with a combination of FHD and overweight had
OR 4.57 (95% CI 3.27, 6.39) for LADA and 24.51 (95% CI
Fig. 1 ORs with 95% CIs for (a)
LADA, (b) type 2 diabetes, (c)
LADAhigh, and (d) LADAlow by
BMI (kg/m2) fitted with restricted
cubic splines using data from
ESTRID 2010–2016. The
reference value is BMI 23 kg/m2
and models were adjusted for age,
sex, FHD, physical activity level
and smoking. Black solid lines
represent the spline line, long
dashed lines represent the 95%
CIs of the spline line and the red
dotted lines represent the linear
line. The histogram at the bottom
of each figure part represents the
distribution of BMI in the study
population. The left y-axes are on
a loge scale
17.82, 33.71) for type 2 diabetes (ESTRID). Corresponding
HR estimates in HUNT were 7.45 (95% CI 4.02, 13.82) and
17.52 (95% CI 14.17, 21.66), respectively (Fig. 2). Interaction
between FHD and overweight was seen for type 2 diabetes
(attributable proportion 0.57, 95% CI 0.49, 0.66), but not for
LADA (attributable proportion 0.06 95% CI −0.25, 0.37) in
ESTRID. Results in HUNT were similar for type 2 diabetes
(attributable proportion 0.58, 95% CI 0.53, 0.63), but stronger
for LADA (attributable proportion 0.37, 95% CI 0.10, 0.64).
Population-attributable risk
Calculations of PAR indicated that 31.0% (95% CI 20.2%,
39.5%) of all individuals with LADA and 81.8% (95% CI
78.7%, 84.1%) of all individuals with type 2 diabetes in the
ESTRID study can be ascribed to overweight/obesity.
Corresponding proportions in HUNT were 56.4% (95% CI
42.3%, 65.5%) (LADA) and 69.9% (95% CI 67.2%, 72.2%)
(type 2 diabetes).
Characteristics of individuals with LADA by category of BMI
In both populations, obese vs normal weight individuals with
LADA had greater insulin production (C-peptide) and were
less often receiving insulin treatment (ESM Tables 4 and 5). In
ESTRID, obese individuals also had lower GADA levels,
better beta cell function (HOMA) and a higher level of insulin
FHD
BMI ≥25
BMI ≥25
+ FHD
Reference
FHD
BMI ≥25
b
FHD
BMI ≥25
Reference
FHD
BMI ≥25
resistance (HOMA). Similar tendencies were seen in HUNT.
However, the differences were not significant (ESM Tables 4
and 5). In ESTRID, obese individuals with LADA were also
more likely to have low-risk HLA genotypes and tended to
less commonly have first-degree relatives with type 1
diabetes. BMI was positively associated with HOMA-IR (2.2%
increase, p = 0.0002) and inversely associated with GADA
(5.1% decrease, p < 0.0001) per BMI unit. In HUNT, results
were similar for HOMA-IR (3.8% increase, p = 0.0077) but
weaker for GADA (0.8% decrease, p = 0.6773).
Discussion
Our findings using data from two large population-based
studies indicate that overweight and obesity are associated with an
increased risk of LADA and that the risk is highest in
individuals with a combination of overweight and FHD. The
association with obesity seemed strongest in LADA with low
GADA, but was apparent also in LADA with higher GADA
levels. The results indicate that LADA in 31–56% of
individuals could be attributed to overweight/obesity, compared with
70–82% of all those with type 2 diabetes.
These findings fit with those of previous cross-sectional
studies, which indicated that individuals with LADA tend to
be obese but leaner than those with type 2 diabetes [
13–19
]
and with previous reports of LADA being characterised by
insulin resistance, but to a lesser extent than type 2 diabetes
[12]. One previous study found that a majority of individuals
with LADA have a lean phenotype [
37
]. One explanation of
this somewhat conflicting result may be the use of a different
age criterion (>25 years), as younger age at onset tends to be
associated with a more type-1-like phenotype [
38
]. In contrast,
the large multicentre ADOPT study found that participants
with LADA and type 2 diabetes were equally overweight/
obese [
39
]. In this study, however, GADA was measured in
individuals with prevalent diabetes without insulin treatment
within the first 3 years of diagnosis. As such, these individuals
with LADA were likely to have a more type-2-like phenotype.
These findings highlight the heterogeneous nature of LADA
and the need for a unified definition.
BMI was positively associated with insulin resistance in
LADA, suggesting that this is an underlying pathway. In
contrast, there was nothing to suggest that excessive weight
would influence autoimmunity per se; there was an inverse
association between BMI and GADA level similar to a
previous report [25]. Reports of type 1 diabetes in children are in
keeping with our data; obesity has been associated with
insulin resistance [
40
], but not with autoimmunity, irrespective of
number and type of diabetes antibodies in the study
participants [
41
]. Our findings fit with the accelerator hypothesis [3],
which proposes that insulin resistance plays a role in
promoting autoimmune diabetes by increasing the insulin demand—
this may accelerate disease onset in individuals with an
ongoing autoimmune process. In the case of mild autoimmunity,
one can hypothesise that factors related to insulin resistance
are more important for progression to overt diabetes. This
could explain why we found a stronger association between
high BMI and less autoimmune LADA and also why the
phenotype of the obese individuals with LADA compared
with those with normal weight, in line with previous reports
[13, 16–18, 25], was more type-2-like, with higher C-peptide
BMI ≥25
+ FHD
BMI ≥25
+ FHD
levels, better beta cell function and a higher level of insulin
resistance. There was a tendency towards a U-shaped
relationship between LADA and BMI. If not occurring by chance, it
may reflect the weight loss often seen in individuals with type
1 diabetes prior to diagnosis as a consequence of insufficient
insulin production.
The association between BMI and LADA was stronger in
the prospective data from HUNT, where BMI was assessed
several years prior to diagnosis, than in the Swedish case–
control data, where BMI was assessed at time of diagnosis.
It is possible that the baseline measurements in HUNT reflect
a more aetiologically relevant exposure window. Self-reported
weight in the case–control study may also have contributed to
dilution of associations. On the other hand, the association
between type 2 diabetes and BMI was stronger in ESTRID.
Another explanation may be that the LADA populations differ
in either genetic or unmeasured phenotypical factors.
We confirm the strong association previously reported of
overweight and obesity with type 2 diabetes [1]. In addition,
we confirm that the combination of overweight and FHD
dramatically increases the risk of type 2 diabetes [21] and show,
for the first time, that the risk of LADA increases substantially
in individuals with FHD and overweight, although the effect is
not as pronounced as for type 2 diabetes. Unfortunately, the
numbers did not allow us to explore interaction with BMI
separately in individuals with a family history of type 1
diabetes vs those with a family history of type 2 diabetes. We have
previously shown that LADA is associated with a family
history of type 2 diabetes, but even more so with a family history
of type 1 diabetes [
42
], which is in line with genetic studies
showing a strong link between LADA and HLA genotypes
associated with type 1 diabetes [12]. Together with the findings
of present study, this supports the idea that LADA is a hybrid
form of diabetes promoted by genes associated with
autoimmunity and lifestyle factors inducing insulin resistance.
Strengths and limitations
The strengths of this study include the large number of
incident cases, detailed information on potential confounders and
the use of two well-defined population-based studies. The
specificity of the GADA assessment was high, but it is
possible that some participants with type 2 diabetes were
misclassified as LADA, i.e. were false positives. This may
contribute to an association with BMI, especially for
LADAlow. It has also been suggested that individuals with
LADA and low GADA are actually false positives [
43
].
However, we found that these individuals differ from those
with type 2 diabetes in several clinical characteristics. Also,
previous studies in the HUNT Study indicate a real impact of
even low and transient levels of GADA, e.g. individuals with
low GADA display lower fasting C-peptide levels than
individuals with type 2 diabetes [
44
]. Still, the importance of
GADA positivity for disease progression in very obese
individuals with low GADA levels is unclear. The sensitivity of
the method and the use of only one autoantibody imply that
some individuals with LADA were classified as GADA
negative, i.e. as having type 2 diabetes. Importantly, GADA is by
far the most common autoantibody in LADA, present in
~90% of all individuals [
45
]. In the HUNT Study, some
individuals had GADA measured several years after diagnosis.
Because GADA can disappear after prolonged disease
duration [
44
], it is possible that some individuals with LADA
therefore appeared GADA negative and were classified as
having type 2 diabetes. Notably, GADA tends to be more
stable in LADA than in type 1 diabetes [12]. Although there
is no unified definition of LADA, the present report is
consistent with currently used criteria [12], with the exception of
Cpeptide which was used in ESTRID as an indicator of
remaining insulin production and can be considered a more objective
measure compared with the frequently used insulin criterion,
i.e. lack of insulin treatment 6–12 months after diagnosis [12].
Estimation of PAR is based on the assumptions of causality
and the absence of measurement errors and bias and should
hence be interpreted with caution. As for generalisability, it
should be noted that PAR is based on the estimated effect size
as well as prevalence of overweight in the population and is,
as such, population specific. The study is based on
populations in Scandinavia, where the incidence of autoimmune
diabetes is high, and the results may be less generalisable to
areas with lower incidence. Last, assessment on insulin
resistance was based on HOMA and even though HOMA has been
validated against the hyperinsulinaemic–euglycemic clamp
with good correlation [
46
], it is still a crude method.
In conclusion, under the assumption of causality, excessive
weight is a strong contributor to development of LADA and
maintaining a healthy weight should be a priority, especially in
the presence of FHD or autoimmunity. As expected, obese
individuals with LADA had a more type-2-like phenotype,
but overweight/obesity was also associated with more
autoimmune LADA. These findings support the hypothesis that even
in the presence of autoimmunity, factors linked to insulin
resistance such as excessive weight could promote the onset of
diabetes.
Acknowledgements We thank all participants in HUNT, ESTRID,
ANDIS and ANDiU as well as administrative personnel, nurses and
research team members from all the studies.
The preliminary results from this work were presented as an abstract at
the 53rd EASD Annual Meeting 2017, Lisbon, Portugal.
Data availability The datasets analysed during the current study are
available from the corresponding author on reasonable request (ESTRID) and
with permission of the HUNT Study by applying to the HUNT Study data
access committee.
Funding ESTRID was funded by grants from the Swedish Medical
Research Council, the Swedish Research Council for Health, Working
life and Welfare, AFA Insurance Company, the Swedish Diabetes
Association and the Novo Nordisk Foundation. ANDIS was funded by
grants from the Swedish Medical Research Council and the European
Research Council Advanced Researcher grant (GA 269045) to LG and
ALF – the Swedish Research Council funding for clinical research.
Funding for ANDiU was provided by the Swedish Medical Research
Council, a strategic research grant from the Swedish Government
(Excellence of Diabetes Research in Sweden [EXODIAB]). The HUNT
Study is a collaboration between the HUNT Research Centre (Faculty of
Medicine and Health Sciences, NTNU, Norwegian University of Science
and Technology), Nord-Trøndelag County Council, Central Norway
Regional Health Authority and the Norwegian Institute of Public
Health. GlaxoSmithKline Norway financially supported the diabetes
study at HUNT2 and HUNT3 through the Norwegian University of
Science and Technology.
Duality of interest The authors declare that there is no duality of interest
associated with this manuscript.
Contribution statement All authors critically revised and approved the
final version of the manuscript. SC, EA, P-OC, VG, LG, RH, MM, BR,
AR and BOÅ contributed to the acquisition of data. VG, BOÅ and TT
contributed to the analysis and interpretation of the data. SC
conceptualised the research objectives and designed the study and
thoroughly revised the manuscript. RH contributed to the objectives of the
study and was responsible for drafting of the manuscript and analysing
the data and takes full responsibility for the accuracy of the analyses and
the work as a whole.
Open Access This article is distributed under the terms of the Creative
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distribution, and reproduction in any medium, provided you give
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Creative Commons license, and indicate if changes were made.
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